Document segmentation typically focuses on splitting a single document (e.g., the transcript of a 30-minute news story) into a linear sequence of segments, each of which represents a coherent sub-topic. There are a number of approaches to single document segmentation, such as detecting segmentation points by specific speech or lexical cues, identifying lexical or semantic changes between adjacent text blocks, and probabilistic topic segmentation. There is also research on taking multiple similar documents (e.g., multiple news articles from different sources reporting the same event) as an input. In such a case, topic segmentation is performed across multiple documents on the shared topics.
The existing body of work on temporal topic analysis incorporates temporal information as an input to improve topic discovery results. For example, a dynamic topic model groups documents by year, and shows how the topics in one year evolve from that of the previous year. In such a model, documents are first grouped by a fixed time frame (e.g., per year). A topic model is then used to derive multiple topics from the documents in each time frame.
Existing efforts in creating topic-based, interactive visual text summarization systems focus on developing visual metaphors and interactions that allow users to understand and explore the derived topics. Existing time-based visual summary approaches are often ad hoc. For example, the temporal segment boundaries are mainly determined by visual constraints, such as the peaks and valleys of a topic layer. If a topic layer has too few natural visual segment boundaries (i.e., the shape of the topic layer is mostly flat), as a heuristic, additional segment boundaries are then introduced using fixed time intervals. Nonetheless, such approaches cannot guarantee that the derived temporal segments capture all significant topic transitions or the prominent topics in a predetermined, fixed time interval. In sum, existing methods do not reveal topic segment explicitly and do not encode flexible temporal and visual constraints.